1,721,001 research outputs found
Modern likelihood inference for the maximum/minimum of a bivariate normal vector
Full text link<p>We consider the use of modern likelihood asymptotics in the construction of confidence intervals for the parameter which determines the skewness of the distribution of the maximum/minimum of an exchangeable bivariate normal random vector. Simulation studies were conducted to investigate the accuracy of the proposed methods and to compare them to available alternatives. Accuracy is evaluated in terms of both coverage probability and expected length of the interval. We furthermore illustrate the suitability of our proposals by means of two data sets, consisting of, respectively, measurements taken on the brains of 10 mono-zygotic twins and measurements of mineral content of bones in the dominant and non-dominant arms for 25 elderly women.</p
Identifying spatial patterns with the Bootstrap ClustGeo technique
No full textBuilding clusters for pattern recognition and analysis of geographical areas can be a useful way to provide relevant information for economic and social decisions. In this paper, we introduce a novel spatial clustering technique, called Bootstrap ClustGeo (BCG), which is a hierarchical approach, based on bootstrap techniques with spatial constraints. We evaluate the performance of the proposed approach BCG through some real case studies and simulations studies with different complexity, by computing Clustering Validation Measures (CVM) and then we compare the approach with the recently proposed ClustGeo (CG). These analyses exhibit the accuracy of BCG, also with respect to CG, in the presented applications, and highlight the great potentiality of this new clustering technique to provide meaningful information for spatial analysis. (C) 2020 Elsevier B.V. All rights reserved
A comparative study on high-dimensional bayesian regression with binary predictors
No full textBayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small high-dimensional data, where the number of observations is very small with respect to the number of predictors, sparsity is assumed and many parameters can be set to values close to zero without affecting the fit of the model. Aim of this work is to develop a comparative analysis to empirically evaluate the performances of several Bayesian regression approaches in these contexts. In this study we assume that the predictors can be expressed only as binary variables coding the presence or the absence of a particular characteristic of the system. This binary structure is often present in many real studies, in particular in laboratory experimentation and in very high-dimension genome wide association studies
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